Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations349
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.6 KiB
Average record size in memory283.4 B

Variable types

Numeric13
Categorical5

Alerts

난방방식 is highly overall correlated with 준공연도High correlation
면적127_139 is highly overall correlated with 면적85_127 and 1 other fieldsHigh correlation
면적85_127 is highly overall correlated with 면적127_139 and 1 other fieldsHigh correlation
실차량수 is highly overall correlated with 총면적 and 1 other fieldsHigh correlation
임대료 is highly overall correlated with 임대보증금High correlation
임대보증금 is highly overall correlated with 면적127_139 and 2 other fieldsHigh correlation
준공연도 is highly overall correlated with 난방방식High correlation
총면적 is highly overall correlated with 실차량수 and 1 other fieldsHigh correlation
총세대수 is highly overall correlated with 실차량수 and 1 other fieldsHigh correlation
승강기설치여부 is highly imbalanced (78.4%)Imbalance
면적85_127 is highly imbalanced (95.8%)Imbalance
면적127_139 is highly imbalanced (95.8%)Imbalance
면적17_26 has 310 (88.8%) zerosZeros
면적26_38 has 226 (64.8%) zerosZeros
면적38_46 has 219 (62.8%) zerosZeros
면적46_51 has 216 (61.9%) zerosZeros
면적51_59 has 219 (62.8%) zerosZeros
면적59_74 has 291 (83.4%) zerosZeros
면적74_85 has 288 (82.5%) zerosZeros
임대보증금 has 36 (10.3%) zerosZeros
임대료 has 36 (10.3%) zerosZeros

Reproduction

Analysis started2024-10-07 13:42:13.073246
Analysis finished2024-10-07 13:42:24.137207
Duration11.06 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

총세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct285
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean544.9341
Minimum1
Maximum2289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:24.199206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.4
Q1178
median491
Q3853
95-th percentile1305.2
Maximum2289
Range2288
Interquartile range (IQR)675

Descriptive statistics

Standard deviation430.75608
Coefficient of variation (CV)0.79047372
Kurtosis0.73614847
Mean544.9341
Median Absolute Deviation (MAD)335
Skewness0.87565633
Sum190182
Variance185550.8
MonotonicityNot monotonic
2024-10-07T22:42:24.280205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
1.1%
1 3
 
0.9%
534 3
 
0.9%
390 3
 
0.9%
630 3
 
0.9%
31 2
 
0.6%
20 2
 
0.6%
875 2
 
0.6%
880 2
 
0.6%
491 2
 
0.6%
Other values (275) 323
92.6%
ValueCountFrequency (%)
1 3
0.9%
2 1
 
0.3%
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 2
0.6%
9 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
14 1
 
0.3%
ValueCountFrequency (%)
2289 1
0.3%
2018 1
0.3%
1937 1
0.3%
1934 1
0.3%
1696 2
0.6%
1647 1
0.3%
1590 1
0.3%
1538 1
0.3%
1489 1
0.3%
1485 1
0.3%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.8281
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-10-07T22:42:24.352206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1995
Q12003
median2008
Q32013
95-th percentile2019
Maximum2022
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2985758
Coefficient of variation (CV)0.0036350601
Kurtosis-0.79708423
Mean2007.8281
Median Absolute Deviation (MAD)5
Skewness-0.22520435
Sum700732
Variance53.269209
MonotonicityNot monotonic
2024-10-07T22:42:24.425206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2007 24
 
6.9%
2011 22
 
6.3%
2010 22
 
6.3%
2008 20
 
5.7%
2009 20
 
5.7%
2018 18
 
5.2%
2016 16
 
4.6%
2013 15
 
4.3%
2004 15
 
4.3%
2002 12
 
3.4%
Other values (21) 165
47.3%
ValueCountFrequency (%)
1992 1
 
0.3%
1993 3
 
0.9%
1994 8
2.3%
1995 11
3.2%
1996 11
3.2%
1997 8
2.3%
1998 9
2.6%
1999 9
2.6%
2000 7
2.0%
2001 7
2.0%
ValueCountFrequency (%)
2022 1
 
0.3%
2021 5
 
1.4%
2020 7
 
2.0%
2019 6
 
1.7%
2018 18
5.2%
2017 12
3.4%
2016 16
4.6%
2015 6
 
1.7%
2014 12
3.4%
2013 15
4.3%

건물형태
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
복도식
200 
계단식
103 
혼합식
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1047
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계단식
2nd row복도식
3rd row계단식
4th row복도식
5th row복도식

Common Values

ValueCountFrequency (%)
복도식 200
57.3%
계단식 103
29.5%
혼합식 46
 
13.2%

Length

2024-10-07T22:42:24.502206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T22:42:24.569206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
복도식 200
57.3%
계단식 103
29.5%
혼합식 46
 
13.2%

Most occurring characters

ValueCountFrequency (%)
349
33.3%
200
19.1%
200
19.1%
103
 
9.8%
103
 
9.8%
46
 
4.4%
46
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
349
33.3%
200
19.1%
200
19.1%
103
 
9.8%
103
 
9.8%
46
 
4.4%
46
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
349
33.3%
200
19.1%
200
19.1%
103
 
9.8%
103
 
9.8%
46
 
4.4%
46
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
349
33.3%
200
19.1%
200
19.1%
103
 
9.8%
103
 
9.8%
46
 
4.4%
46
 
4.4%

난방방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
개별
202 
지역
128 
중앙
 
19

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters698
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개별
2nd row개별
3rd row개별
4th row지역
5th row개별

Common Values

ValueCountFrequency (%)
개별 202
57.9%
지역 128
36.7%
중앙 19
 
5.4%

Length

2024-10-07T22:42:24.640206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T22:42:24.706213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
개별 202
57.9%
지역 128
36.7%
중앙 19
 
5.4%

Most occurring characters

ValueCountFrequency (%)
202
28.9%
202
28.9%
128
18.3%
128
18.3%
19
 
2.7%
19
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
202
28.9%
202
28.9%
128
18.3%
128
18.3%
19
 
2.7%
19
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
202
28.9%
202
28.9%
128
18.3%
128
18.3%
19
 
2.7%
19
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
202
28.9%
202
28.9%
128
18.3%
128
18.3%
19
 
2.7%
19
 
2.7%

승강기설치여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
1
337 
0
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters349
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

Length

2024-10-07T22:42:24.777213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T22:42:24.836212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

Most occurring characters

ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 337
96.6%
0 12
 
3.4%

실차량수
Real number (ℝ)

HIGH CORRELATION 

Distinct292
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.26074
Minimum21
Maximum1657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:24.906723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile51
Q1255
median498
Q3814
95-th percentile1258.6
Maximum1657
Range1636
Interquartile range (IQR)559

Descriptive statistics

Standard deviation373.77517
Coefficient of variation (CV)0.67558592
Kurtosis-0.15466009
Mean553.26074
Median Absolute Deviation (MAD)269
Skewness0.64654756
Sum193088
Variance139707.88
MonotonicityNot monotonic
2024-10-07T22:42:25.000722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
654 4
 
1.1%
109 3
 
0.9%
411 3
 
0.9%
258 3
 
0.9%
569 3
 
0.9%
157 2
 
0.6%
113 2
 
0.6%
568 2
 
0.6%
104 2
 
0.6%
780 2
 
0.6%
Other values (282) 323
92.6%
ValueCountFrequency (%)
21 1
0.3%
22 2
0.6%
23 1
0.3%
24 2
0.6%
27 1
0.3%
28 1
0.3%
29 2
0.6%
31 1
0.3%
32 1
0.3%
35 1
0.3%
ValueCountFrequency (%)
1657 1
0.3%
1635 1
0.3%
1608 1
0.3%
1565 1
0.3%
1522 1
0.3%
1487 1
0.3%
1485 1
0.3%
1440 1
0.3%
1421 1
0.3%
1384 1
0.3%

총면적
Real number (ℝ)

HIGH CORRELATION 

Distinct345
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37142.938
Minimum68.93
Maximum150126.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.095723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum68.93
5-th percentile1338.1555
Q111293.832
median32190.36
Q356096.792
95-th percentile91140.237
Maximum150126.86
Range150057.93
Interquartile range (IQR)44802.96

Descriptive statistics

Standard deviation29492.215
Coefficient of variation (CV)0.79401946
Kurtosis0.48291103
Mean37142.938
Median Absolute Deviation (MAD)21635.995
Skewness0.83191853
Sum12962885
Variance8.6979075 × 108
MonotonicityNot monotonic
2024-10-07T22:42:25.188722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43060.9088 2
 
0.6%
36521.362 2
 
0.6%
5143.414 2
 
0.6%
15641.1528 2
 
0.6%
6023.7683 1
 
0.3%
37742.1594 1
 
0.3%
27555.6094 1
 
0.3%
52884.5483 1
 
0.3%
52143.1351 1
 
0.3%
35326.3126 1
 
0.3%
Other values (335) 335
96.0%
ValueCountFrequency (%)
68.93 1
0.3%
78 1
0.3%
115.788 1
0.3%
163.3726 1
0.3%
233.085 1
0.3%
324.9192 1
0.3%
427.71 1
0.3%
460.2816 1
0.3%
509.6 1
0.3%
543.0268 1
0.3%
ValueCountFrequency (%)
150126.8632 1
0.3%
142698.5412 1
0.3%
135068.8832 1
0.3%
117542.5519 1
0.3%
113941.5009 1
0.3%
113921.3586 1
0.3%
106511.9938 1
0.3%
104504.2246 1
0.3%
104129.1532 1
0.3%
99444.8664 1
0.3%

면적17_26
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.82235
Minimum0
Maximum1181
Zeros310
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.270723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile140
Maximum1181
Range1181
Interquartile range (IQR)0

Descriptive statistics

Standard deviation122.82659
Coefficient of variation (CV)4.7566
Kurtosis49.803786
Mean25.82235
Median Absolute Deviation (MAD)0
Skewness6.6577711
Sum9012
Variance15086.371
MonotonicityNot monotonic
2024-10-07T22:42:25.353723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 310
88.8%
40 3
 
0.9%
140 2
 
0.6%
66 2
 
0.6%
1181 1
 
0.3%
537 1
 
0.3%
22 1
 
0.3%
80 1
 
0.3%
1071 1
 
0.3%
18 1
 
0.3%
Other values (26) 26
 
7.4%
ValueCountFrequency (%)
0 310
88.8%
3 1
 
0.3%
7 1
 
0.3%
10 1
 
0.3%
14 1
 
0.3%
18 1
 
0.3%
20 1
 
0.3%
22 1
 
0.3%
26 1
 
0.3%
27 1
 
0.3%
ValueCountFrequency (%)
1181 1
0.3%
1071 1
0.3%
956 1
0.3%
616 1
0.3%
576 1
0.3%
537 1
0.3%
516 1
0.3%
430 1
0.3%
352 1
0.3%
310 1
0.3%

면적26_38
Real number (ℝ)

ZEROS 

Distinct108
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.05158
Minimum0
Maximum1344
Zeros226
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.447723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3178
95-th percentile536
Maximum1344
Range1344
Interquartile range (IQR)178

Descriptive statistics

Standard deviation226.1695
Coefficient of variation (CV)1.9322209
Kurtosis8.8921263
Mean117.05158
Median Absolute Deviation (MAD)0
Skewness2.6987264
Sum40851
Variance51152.641
MonotonicityNot monotonic
2024-10-07T22:42:25.539230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 226
64.8%
298 4
 
1.1%
89 3
 
0.9%
56 2
 
0.6%
100 2
 
0.6%
345 2
 
0.6%
557 2
 
0.6%
590 2
 
0.6%
16 2
 
0.6%
290 2
 
0.6%
Other values (98) 102
29.2%
ValueCountFrequency (%)
0 226
64.8%
1 2
 
0.6%
16 2
 
0.6%
25 1
 
0.3%
26 1
 
0.3%
30 1
 
0.3%
35 1
 
0.3%
54 1
 
0.3%
56 2
 
0.6%
60 1
 
0.3%
ValueCountFrequency (%)
1344 1
0.3%
1304 1
0.3%
1286 1
0.3%
1252 1
0.3%
1054 1
0.3%
973 1
0.3%
841 1
0.3%
824 1
0.3%
730 1
0.3%
709 1
0.3%

면적38_46
Real number (ℝ)

ZEROS 

Distinct108
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.75072
Minimum0
Maximum1258
Zeros219
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.626230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3158
95-th percentile508.8
Maximum1258
Range1258
Interquartile range (IQR)158

Descriptive statistics

Standard deviation191.05946
Coefficient of variation (CV)1.8066966
Kurtosis6.2575338
Mean105.75072
Median Absolute Deviation (MAD)0
Skewness2.3011647
Sum36907
Variance36503.716
MonotonicityNot monotonic
2024-10-07T22:42:25.717231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219
62.8%
40 4
 
1.1%
119 3
 
0.9%
89 2
 
0.6%
462 2
 
0.6%
248 2
 
0.6%
216 2
 
0.6%
244 2
 
0.6%
130 2
 
0.6%
540 2
 
0.6%
Other values (98) 109
31.2%
ValueCountFrequency (%)
0 219
62.8%
9 1
 
0.3%
16 1
 
0.3%
19 1
 
0.3%
22 1
 
0.3%
26 1
 
0.3%
31 1
 
0.3%
32 1
 
0.3%
37 1
 
0.3%
40 4
 
1.1%
ValueCountFrequency (%)
1258 1
0.3%
960 1
0.3%
871 1
0.3%
818 1
0.3%
802 1
0.3%
702 1
0.3%
676 1
0.3%
638 1
0.3%
630 1
0.3%
590 1
0.3%

면적46_51
Real number (ℝ)

ZEROS 

Distinct119
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.78797
Minimum0
Maximum1138
Zeros216
Zeros (%)61.9%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.811231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3177
95-th percentile514.8
Maximum1138
Range1138
Interquartile range (IQR)177

Descriptive statistics

Standard deviation196.02856
Coefficient of variation (CV)1.722753
Kurtosis5.0610077
Mean113.78797
Median Absolute Deviation (MAD)0
Skewness2.1163902
Sum39712
Variance38427.196
MonotonicityNot monotonic
2024-10-07T22:42:25.898739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 216
61.9%
356 3
 
0.9%
180 3
 
0.9%
143 2
 
0.6%
60 2
 
0.6%
304 2
 
0.6%
78 2
 
0.6%
264 2
 
0.6%
174 2
 
0.6%
110 2
 
0.6%
Other values (109) 113
32.4%
ValueCountFrequency (%)
0 216
61.9%
1 1
 
0.3%
2 1
 
0.3%
6 1
 
0.3%
10 1
 
0.3%
16 1
 
0.3%
30 1
 
0.3%
32 1
 
0.3%
38 1
 
0.3%
46 1
 
0.3%
ValueCountFrequency (%)
1138 1
0.3%
1099 1
0.3%
903 1
0.3%
816 1
0.3%
801 1
0.3%
714 1
0.3%
700 1
0.3%
670 1
0.3%
630 1
0.3%
627 1
0.3%

면적51_59
Real number (ℝ)

ZEROS 

Distinct117
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.85673
Minimum0
Maximum1350
Zeros219
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:25.985251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3134
95-th percentile592.4
Maximum1350
Range1350
Interquartile range (IQR)134

Descriptive statistics

Standard deviation222.29745
Coefficient of variation (CV)1.9873408
Kurtosis8.8195675
Mean111.85673
Median Absolute Deviation (MAD)0
Skewness2.7607918
Sum39038
Variance49416.158
MonotonicityNot monotonic
2024-10-07T22:42:26.077252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219
62.8%
356 2
 
0.6%
162 2
 
0.6%
260 2
 
0.6%
84 2
 
0.6%
132 2
 
0.6%
150 2
 
0.6%
187 2
 
0.6%
3 2
 
0.6%
145 2
 
0.6%
Other values (107) 112
32.1%
ValueCountFrequency (%)
0 219
62.8%
2 1
 
0.3%
3 2
 
0.6%
4 1
 
0.3%
6 1
 
0.3%
9 1
 
0.3%
12 1
 
0.3%
17 1
 
0.3%
18 1
 
0.3%
28 1
 
0.3%
ValueCountFrequency (%)
1350 1
0.3%
1303 1
0.3%
1301 1
0.3%
901 1
0.3%
890 1
0.3%
886 1
0.3%
879 1
0.3%
840 1
0.3%
818 1
0.3%
801 1
0.3%

면적59_74
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.461318
Minimum0
Maximum1228
Zeros291
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:26.279496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile119.2
Maximum1228
Range1228
Interquartile range (IQR)0

Descriptive statistics

Standard deviation112.8224
Coefficient of variation (CV)4.1084115
Kurtosis50.380153
Mean27.461318
Median Absolute Deviation (MAD)0
Skewness6.4375793
Sum9584
Variance12728.893
MonotonicityNot monotonic
2024-10-07T22:42:26.384496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 291
83.4%
3 3
 
0.9%
43 2
 
0.6%
1 2
 
0.6%
118 2
 
0.6%
36 2
 
0.6%
91 2
 
0.6%
68 2
 
0.6%
7 1
 
0.3%
469 1
 
0.3%
Other values (41) 41
 
11.7%
ValueCountFrequency (%)
0 291
83.4%
1 2
 
0.6%
2 1
 
0.3%
3 3
 
0.9%
4 1
 
0.3%
5 1
 
0.3%
7 1
 
0.3%
11 1
 
0.3%
15 1
 
0.3%
17 1
 
0.3%
ValueCountFrequency (%)
1228 1
0.3%
756 1
0.3%
702 1
0.3%
694 1
0.3%
495 1
0.3%
470 1
0.3%
469 1
0.3%
443 1
0.3%
408 1
0.3%
372 1
0.3%

면적74_85
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.484241
Minimum0
Maximum992
Zeros288
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:26.472497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile299.2
Maximum992
Range992
Interquartile range (IQR)0

Descriptive statistics

Standard deviation141.65075
Coefficient of variation (CV)3.3341951
Kurtosis18.124996
Mean42.484241
Median Absolute Deviation (MAD)0
Skewness4.1613592
Sum14827
Variance20064.934
MonotonicityNot monotonic
2024-10-07T22:42:26.561496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 288
82.5%
404 2
 
0.6%
6 2
 
0.6%
67 2
 
0.6%
31 2
 
0.6%
216 1
 
0.3%
456 1
 
0.3%
12 1
 
0.3%
674 1
 
0.3%
298 1
 
0.3%
Other values (48) 48
 
13.8%
ValueCountFrequency (%)
0 288
82.5%
1 1
 
0.3%
5 1
 
0.3%
6 2
 
0.6%
11 1
 
0.3%
12 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
20 1
 
0.3%
23 1
 
0.3%
ValueCountFrequency (%)
992 1
0.3%
812 1
0.3%
793 1
0.3%
792 1
0.3%
728 1
0.3%
715 1
0.3%
688 1
0.3%
674 1
0.3%
656 1
0.3%
490 1
0.3%

면적85_127
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
0
346 
407
 
1
302
 
1
173
 
1

Length

Max length3
Median length1
Mean length1.017192
Min length1

Characters and Unicode

Total characters355
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 346
99.1%
407 1
 
0.3%
302 1
 
0.3%
173 1
 
0.3%

Length

2024-10-07T22:42:26.652496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T22:42:26.725497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 346
99.1%
407 1
 
0.3%
302 1
 
0.3%
173 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 348
98.0%
7 2
 
0.6%
3 2
 
0.6%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 348
98.0%
7 2
 
0.6%
3 2
 
0.6%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 348
98.0%
7 2
 
0.6%
3 2
 
0.6%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 348
98.0%
7 2
 
0.6%
3 2
 
0.6%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

면적127_139
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
0
346 
26
 
1
62
 
1
40
 
1

Length

Max length2
Median length1
Mean length1.008596
Min length1

Characters and Unicode

Total characters352
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 346
99.1%
26 1
 
0.3%
62 1
 
0.3%
40 1
 
0.3%

Length

2024-10-07T22:42:26.799497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T22:42:26.866857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 346
99.1%
26 1
 
0.3%
62 1
 
0.3%
40 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 347
98.6%
2 2
 
0.6%
6 2
 
0.6%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 347
98.6%
2 2
 
0.6%
6 2
 
0.6%
4 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 347
98.6%
2 2
 
0.6%
6 2
 
0.6%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 347
98.6%
2 2
 
0.6%
6 2
 
0.6%
4 1
 
0.3%

임대보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct304
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23829830
Minimum0
Maximum1.8982186 × 108
Zeros36
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:26.940848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112561667
median19076000
Q328377000
95-th percentile63224800
Maximum1.8982186 × 108
Range1.8982186 × 108
Interquartile range (IQR)15815333

Descriptive statistics

Standard deviation22428190
Coefficient of variation (CV)0.94118127
Kurtosis15.507298
Mean23829830
Median Absolute Deviation (MAD)7190000
Skewness3.1579057
Sum8.3166106 × 109
Variance5.0302369 × 1014
MonotonicityNot monotonic
2024-10-07T22:42:27.034848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
10.3%
24655500 3
 
0.9%
27465500 2
 
0.6%
30968666.67 2
 
0.6%
16755500 2
 
0.6%
11886000 2
 
0.6%
32443000 2
 
0.6%
12528000 2
 
0.6%
69000000 2
 
0.6%
55000000 2
 
0.6%
Other values (294) 294
84.2%
ValueCountFrequency (%)
0 36
10.3%
2205000 1
 
0.3%
2515500 1
 
0.3%
4942000 1
 
0.3%
5158333.333 1
 
0.3%
6485000 1
 
0.3%
7595571.429 1
 
0.3%
7628000 1
 
0.3%
8092875 1
 
0.3%
8198000 1
 
0.3%
ValueCountFrequency (%)
189821857.1 1
0.3%
170746375 1
0.3%
114770250 1
0.3%
113172333.3 1
0.3%
101516666.7 1
0.3%
100389000 1
0.3%
97085500 1
0.3%
96250000 1
0.3%
89764000 1
0.3%
87417000 1
0.3%

임대료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct305
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198457.56
Minimum0
Maximum950305
Zeros36
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-10-07T22:42:27.129848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112905
median171766.67
Q3227564
95-th percentile577871.83
Maximum950305
Range950305
Interquartile range (IQR)114659

Descriptive statistics

Standard deviation159575.92
Coefficient of variation (CV)0.80408081
Kurtosis3.9639483
Mean198457.56
Median Absolute Deviation (MAD)57511.667
Skewness1.8022532
Sum69261689
Variance2.5464473 × 1010
MonotonicityNot monotonic
2024-10-07T22:42:27.216848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
10.3%
134192.5 2
 
0.6%
218475 2
 
0.6%
169500 2
 
0.6%
124830 2
 
0.6%
93000 2
 
0.6%
375000 2
 
0.6%
162666.6667 2
 
0.6%
192250 2
 
0.6%
620000 2
 
0.6%
Other values (295) 295
84.5%
ValueCountFrequency (%)
0 36
10.3%
39920 1
 
0.3%
44086.66667 1
 
0.3%
48360 1
 
0.3%
48670 1
 
0.3%
50040 1
 
0.3%
52000 1
 
0.3%
53660 1
 
0.3%
56855 1
 
0.3%
62775 1
 
0.3%
ValueCountFrequency (%)
950305 1
0.3%
815355.7143 1
0.3%
803672.5 1
0.3%
767010 1
0.3%
759490 1
0.3%
744450 1
0.3%
715000 1
0.3%
699330 1
0.3%
642930 1
0.3%
634472.5 1
0.3%

Interactions

2024-10-07T22:42:23.037694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.467479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.267905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.037457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.830106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.639484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.508526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.307527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.079532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.833588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.717116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.486151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.232694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.089694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.521479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.322458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.097458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.886161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.693484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.569526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.363526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.131534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.893589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.771115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.538694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.288696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.144695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.576485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.379458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.158458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.948161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.753484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.630526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.421526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.187533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.951588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.829226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.595694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.349694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.205695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.637496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.442458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.220457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.014161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.818991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.694527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.485525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.248534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.018589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.890226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.655694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.415694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.264694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.762507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.504459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.285459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.077163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.883503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.760525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.548527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.307533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.082097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.952227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.716694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.480694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.320695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.817799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.563458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.344458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.139670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.940504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.822526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.607526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.365533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.144605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.008226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.774694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.541695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.378694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.875095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.624457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.406459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.207670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.093014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.886526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.666526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.422533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.206606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.069226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.834695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.606695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.435694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.930267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.682458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.464458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.269669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.150527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.949526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.723525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.479534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.360117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.127226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.891695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.673696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.492695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:13.985269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.740458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.524458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.332670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.208527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.008526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.783526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.532588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.416116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.184227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.948694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.732694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.552694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.044430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.802458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.588459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.393669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.271526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.069526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.846526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.594589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.478116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.243151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.008693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.796696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.609694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.098291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.862458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.652458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.458669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.328526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.130525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.903533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.650588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.538117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.302150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.063694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.858694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.662694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.151268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.919459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.708458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-10-07T22:42:17.385526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.188526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.963534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.708588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.596117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.360150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.119695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.917695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:23.824694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.215267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:14.982458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:15.773458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:16.584691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:17.452526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:18.251526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.025533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:19.774589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:20.661115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:21.423151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.179694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-07T22:42:22.979694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-07T22:42:27.281276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
건물형태난방방식면적127_139면적17_26면적26_38면적38_46면적46_51면적51_59면적59_74면적74_85면적85_127승강기설치여부실차량수임대료임대보증금준공연도총면적총세대수
건물형태1.0000.1530.0420.0760.2550.2410.2630.2800.1680.3010.0420.1860.2230.3980.3040.3120.2270.217
난방방식0.1531.0000.0000.2400.1360.1810.0580.0100.0760.1350.0000.1050.2690.2560.2340.5220.2580.214
면적127_1390.0420.0001.0000.0000.0000.0000.0000.0000.0000.3001.0000.0000.2520.4740.8460.0000.4020.000
면적17_260.0760.2400.0001.0000.157-0.191-0.080-0.155-0.157-0.1620.0000.000-0.228-0.177-0.2050.204-0.155-0.059
면적26_380.2550.1360.0000.1571.0000.0380.153-0.148-0.271-0.3270.0000.1580.0350.025-0.0800.0940.1520.295
면적38_460.2410.1810.000-0.1910.0381.0000.214-0.008-0.229-0.3400.0000.0000.0960.003-0.005-0.2660.2710.360
면적46_510.2630.0580.000-0.0800.1530.2141.0000.142-0.199-0.3380.0000.0000.2870.1530.1730.0130.3990.457
면적51_590.2800.0100.000-0.155-0.148-0.0080.1421.0000.142-0.2730.0000.0000.3050.2640.4590.0290.3800.355
면적59_740.1680.0760.000-0.157-0.271-0.229-0.1990.1421.0000.2700.0000.0000.2180.2040.3030.1640.112-0.004
면적74_850.3010.1350.300-0.162-0.327-0.340-0.338-0.2730.2701.0000.3000.0000.2530.0380.0400.262-0.013-0.212
면적85_1270.0420.0001.0000.0000.0000.0000.0000.0000.0000.3001.0000.0000.2520.4740.8460.0000.4020.000
승강기설치여부0.1860.1050.0000.0000.1580.0000.0000.0000.0000.0000.0001.0000.1560.0960.0490.2510.1320.155
실차량수0.2230.2690.252-0.2280.0350.0960.2870.3050.2180.2530.2520.1561.0000.3550.4450.2480.7780.687
임대료0.3980.2560.474-0.1770.0250.0030.1530.2640.2040.0380.4740.0960.3551.0000.7790.2790.4670.407
임대보증금0.3040.2340.846-0.205-0.080-0.0050.1730.4590.3030.0400.8460.0490.4450.7791.0000.3640.4720.390
준공연도0.3120.5220.0000.2040.094-0.2660.0130.0290.1640.2620.0000.2510.2480.2790.3641.0000.1420.070
총면적0.2270.2580.402-0.1550.1520.2710.3990.3800.112-0.0130.4020.1320.7780.4670.4720.1421.0000.956
총세대수0.2170.2140.000-0.0590.2950.3600.4570.355-0.004-0.2120.0000.1550.6870.4070.3900.0700.9561.000

Missing values

2024-10-07T22:42:23.910694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-07T22:42:24.065206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

총세대수준공연도건물형태난방방식승강기설치여부실차량수총면적면적17_26면적26_38면적38_46면적46_51면적51_59면적59_74면적74_85면적85_127면적127_139임대보증금임대료
0782013계단식개별11096023.7683000035430005.696200e+07642930.000000
1352013복도식개별1351569.166803500000006.306200e+07470100.000000
2882013계단식개별1887180.139600008800007.219000e+07586540.000000
34772014복도식지역194347058.927300000199278001.015167e+08950305.000000
4152013복도식개별121543.026814100000005.522750e+07340148.333333
5692014계단식개별18304802.4651026004300002.878033e+07450920.000000
6862011계단식개별18926409.9462004004600000.000000e+000.000000
7392007계단식개별17444341.870000000039000.000000e+000.000000
8262012계단식지역13631967.505800260000000.000000e+000.000000
9462007계단식개별16325031.332800000046000.000000e+000.000000
총세대수준공연도건물형태난방방식승강기설치여부실차량수총면적면적17_26면적26_38면적38_46면적46_51면적51_59면적59_74면적74_85면적85_127면적127_139임대보증금임대료
3393062018복도식지역121015641.15280246060000001.252800e+07162666.666667
3401302021복도식개별11137436.72002262046000001.012533e+07157220.000000
341402021복도식개별1321551.240040000000009.697000e+0689270.000000
3424202001복도식개별126724979.474802521680000001.547200e+0794375.000000
3438351995복도식지역111434655.840053729800000001.051267e+07138996.666667
34414851993복도식중앙129864622.2500118129800600007.595571e+06104975.714286
34513861993복도식중앙125857616.81001071298001700008.092875e+06111848.750000
3469561994복도식지역124337398.7200956000000009.931000e+06134540.000000
3471202020복도식개별1475581.8024665400000002.515500e+0650040.000000
3484471994복도식중앙17819383.410014929800000007.628000e+06125010.000000